• Priti Aher
• Vaishnavi Garghate
• Siddhi Shimpi
• Shivani Jadhav

It is difficult to make generalizations about information as its whole since it typically consists of multiple characteristic or multimodal subsets, as is the case in many real-world settings. This sort of information is called "Multiview" data. Publications, for instance, may be made available in many languages, while photos posted across social media platforms virtually always include both the image and a tag explaining what the image symbolizes. The notion that identical information can be viewed in numerous ways has led to a recent uptick in awareness of Multiview training. Numerous promising applications exist for the synthesis of Multiview information, such as multitask learning, clinical applications, object categorization, knowledge representation, clustering, and categorization. Many studies have been conducted in this area, paving the way for the incorporation of Multiview solutions into a wide range of scenarios; nonetheless, their use in the development of Multiview clusters of Alzheimer's disease images has been limited. Consequently, researchers have been analyzing a collection of Multiview classification studies over the last several years to find their weaknesses. Our method for forming Multiview clusters for Alzheimer's disease is based on the combination of image pair selection, data matrices, fusion, a feature list, and fuzzy classification.

Image Pair Selection, Data Matrices, Parameter Estimation, Feature List, Fuzzy Classification, Multiview Clustering.

"Multiview Clustering for Alzheimer’s Progression through Deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 9, page no.d452-d460, September-2023, Available :http://www.jetir.org/papers/JETIR2309345.pdf

"Multiview Clustering for Alzheimer’s Progression through Deep learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 9, page no. ppd452-d460, September-2023, Available at : http://www.jetir.org/papers/JETIR2309345.pdf